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Record W2191330557 · doi:10.7202/1070364ar

Performativity and Affect in Education

2020· article· en· W2191330557 on OpenAlexaffvenue
Claudia W. Ruitenberg

Bibliographic record

VenuePhilosophical Inquiry in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerformativityAffect (linguistics)FeelingSociologyAffect theoryEpistemologyAestheticsCurriculumSocial psychologyPsychologyPedagogyGender studiesPhilosophy

Abstract

fetched live from OpenAlex

This essay examines the concept of performativity in relation to what are perceived to be reasonable and unreasonable affective responses to discourse. It considers how discourse, especially in classrooms and other educational contexts, produces effects, and how it is that those effects are sometimes seen as attached to the discourse, and sometimes as attached to the person who perceives and displays the effect. When discourse produces strong affective responses, sometimes the discourse itself is seen as unreasonable and in need of socializing (e.g., racist and homophobic slurs), and sometimes the person is perceived as “overreacting” to language that is not considered inherently affectively charged. Such distinctions, whether made explicitly or not, shape educational contexts and offer a hidden curriculum of “appropriate” affect. The essay traces the concept of performativity through the work of Austin, Derrida, Butler, and Cavell, and then extends it to affect theory to see how performativity can help us think through the provocation or production of feelings beyond individual psychological explanations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.020
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.112
GPT teacher head0.404
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2020
Admission routes2
Has abstractyes

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